This section describes the architecture of our network to compute a discriminative global descriptor of an input radar scan image.
The high-level network architecture is shown in Fig. REF .
It consists of two parts: local feature extraction network followed by the generalized-mean (GeM) [1]} pooling layer. Our initial experiments proved that GeM yields better results compared to commonly used NetVLAD [2]} pooling. One of the reasons is much smaller number of learnable parameters that reduces the risk of overfitting to a moderately-sized training set.
The input to the network is a single-channel radar scan image in polar coordinates.
The scan image is processed using a 2D convolutional network modelled after FPN [3]} design pattern.
Upper part of the network, with left-to-right data flow, contains five convolutional blocks producing 2D feature maps with
decreasing spatial resolution and increasing receptive field.
The bottom part, with right-to-left data flow, contains a transposed convolution generating an upsampled feature map.
Upsampled feature map is concatenated with the skipped features from the corresponding block in the upper pass using a lateral connection.
Such design is intended to produce a feature map with higher spatial resolution, having a large receptive field.
Our experiments proved its advantage over a simple convolutional architecture with one-directional data flow.
Table REF  shows details of each network block. The first convolutional block (2dConv\(_0\)) has a bigger 5x5 kernel, in order to aggregate information from a larger neighbourhood. Subsequent blocks (2dConv\(_1 \ldots \) 2dConv\(_4\))
are made of a stride two convolution, which decreases spatial resolution by two, followed by residual block consisting
of two convolutional layers with 3x3 kernel and ECA [4]} channel attention layer.
All convolutional layers are followed by batch normalization [5]} layer and ReLU non-linearity.
Two 1xConv blocks have the same structure, both contain a single convolutional layer with 1x1 kernel. The aim of these
blocks is to unify the number of channels in feature maps produced by the blocks in the left-to-right, before they are merged with feature maps from the right-to-left pass through the network.
The bottom part of the network (left-to-right pass)consists of a single transposed convolution layer (2dTConv\(_{4}\)) with 2x2 kernel and stride 2.
The feature map \(\mathcal {F}_h\)  computed by the feature extraction network is pooled with generalized-mean (GeM) [1]} pooling to produce a radar scan descriptor \(\mathcal {H}\)
